New energy electric power storage collaborative scheduling method and system based on spot market

Abstract

The invention discloses a new energy electric power energy storage collaborative scheduling method and system based on a spot market, and relates to the technical field of electric power system optimization energy storage, and the method comprises the steps: collecting the historical data of an electric power system and spot market prediction data; performing target modeling, and inputting initial data; optimizing the model, and performing day-ahead scheduling and intra-day scheduling; and real-time monitoring and prediction updating are carried out, and rolling optimization scheduling is carried out. The method has the beneficial effect that charging and discharging optimization is carried out in combination with the state of the energy storage system. The energy storage system can participate in frequency modulation service and quickly respond when frequency fluctuation occurs in the power grid. According to the process, the economic benefit of the energy storage system is maximized through real-time scheduling and frequency modulation service, and the stability of the power grid is improved. Through combination of electricity price arbitrage, frequency modulation service and rolling optimization, the energy storage system can effectively deal with uncertainty in a complex market environment, and the goal of revenue maximization is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization energy management, and specifically to a new energy power storage coordinated scheduling method and system based on the spot market. Background Art

[0002] As the global energy structure transforms towards low-carbon and sustainable development, the large-scale access to renewable energy (such as wind and solar) has brought huge challenges to the power system. These renewable energy sources are intermittent and uncertain, which makes the power system face the risk of load fluctuations and imbalance between supply and demand. Therefore, how to effectively use renewable energy in the power market while ensuring the stability of the power grid has become an important issue.

[0003] In order to meet these challenges, energy storage technologies (such as battery energy storage and pumped storage) have gradually become key technologies in power systems. Energy storage systems can achieve smooth regulation of electricity and maximize economic benefits by storing electricity when electricity prices are low and discharging it when electricity prices are high. In addition, electricity markets (such as day-ahead markets and spot markets) provide a dynamic environment for buying and selling electricity, allowing energy storage systems to play an important role in responding to fluctuations in electricity prices and renewable energy generation. In this context, coordinated scheduling of energy storage systems has become a key technology for optimizing the operation of power systems, aiming to maximize the utilization efficiency of renewable energy, improve the flexibility of the power grid, and ensure maximum economic benefits through dynamic optimization and rolling scheduling.

[0004] Energy storage systems can participate in scheduling optimization in the power market through electricity price arbitrage strategies. They can charge when the electricity price is low and discharge when the electricity price is high, thereby maximizing economic benefits. This strategy is usually based on linear programming or mixed integer programming for optimal scheduling, and combines the price fluctuations of the day-ahead market and the spot market to perform charging and discharging operations. The participation mechanism of energy storage systems in the power market is still not flexible enough, especially in the spot market. How energy storage systems can effectively participate in price arbitrage, frequency regulation services and other market mechanisms has not yet been fully standardized. In addition, market rules and fluctuations in electricity prices make the benefits of energy storage system scheduling uncertain. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: with the rapid development of the electricity market and new energy technologies, how to better integrate the energy storage system with the electricity market to improve the flexibility and robustness of the system.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a new energy power storage coordinated dispatching method based on the spot market, comprising:

[0008] Collect power system historical data and spot market forecast data;

[0009] Conduct target modeling and input initial data;

[0010] Optimize the model to perform day-ahead and intraday scheduling;

[0011] Real-time monitoring and forecasting updates, and rolling optimization scheduling.

[0012] As a preferred solution of the new energy power storage coordinated dispatching method based on the spot market described in the present invention, wherein:

[0013] The power system historical data and spot market forecast data include new energy power generation forecast data, power market data, energy storage system data, and load demand data;

[0014] Electricity market data includes day-ahead market electricity price forecast data, real-time market electricity price data, and historical market price data;

[0015] Clean and process the acquired historical data and forecast data, remove abnormal data, and fill in missing values; use historical data to analyze the fluctuation range of sources of uncertainty.

[0016] As a preferred solution of the new energy power storage coordinated dispatching method based on the spot market described in the present invention, wherein:

[0017] The target modeling includes defining optimization targets and defining optimization constraints according to specific requirements and physical constraints of the power system;

[0018] The optimization goals include maximizing revenue, minimizing costs, and ensuring system stability. After the model is built, the forecast data of new energy power generation, electricity market data, energy storage system data, and load demand data are input. The forecast data uses an ultra-short-term forecasting method to ensure forecast accuracy.

[0019] As a preferred solution of the method for coordinated dispatching of new energy power storage based on the spot market described in the present invention, the day-ahead dispatching and intraday dispatching include: in the day-ahead dispatching stage, the system needs to formulate a power dispatching plan in advance; due to the uncertainty of the fluctuation of new energy and electricity prices, the day-ahead dispatching needs to take into account the uncertainty factors and determine the optimal decision through a robust optimization method;

[0020] Using robust optimization methods, the uncertainty of renewable energy generation and electricity prices is considered by setting uncertainty sets; defining uncertainty sets All possible scenarios in the set need to satisfy robustness constraints and perform uncertainty modeling;

[0021] During the intraday dispatch stage, the power dispatch plan is continuously adjusted using a rolling optimization method based on more accurate short-term forecasts and real-time market prices to ensure the economy and stability of the system at every moment; the intraday dispatch stage is divided into multiple time periods, and each time period is reached, the dispatch optimization is re-performed based on the latest renewable energy forecast data and market electricity prices.

[0022] As a preferred solution of the method for coordinated dispatching of new energy power storage based on the spot market described in the present invention, wherein: the rolling optimization dispatching includes setting a rolling optimization target, and the rolling optimization target of the energy storage system is to maximize the benefits obtained by the energy storage system through electricity price arbitrage and frequency regulation services in the next time period;

[0023] Perform short-term forecast updates, update the new energy power generation forecast value and electricity price forecast value in real time in each time period, and optimize the real-time scheduling: use the rolling optimization method to adjust the system's charging and discharging strategy according to the latest data in each time period; the goal of rolling optimization is to maximize the system's real-time benefits under new forecast conditions through short-term adjustments; rolling optimization scheduling needs to meet the defined power balance, energy storage system capacity and charging and discharging power constraints.

[0024] As a preferred solution of the new energy power storage coordinated dispatching method based on the spot market described in the present invention, the uncertainty modeling includes the uncertainty of renewable energy generation, setting Represents the renewable energy output at time t, and the predicted output is P r,t , then its uncertainty is expressed as:

[0025]

[0026] Among them, δ r,t Represents the disturbance of renewable energy power generation fluctuations and satisfies the uncertainty set The constraints of electricity price uncertainty, is the electricity price at time t, and the predicted electricity price is λ t , then the uncertainty formula is expressed as:

[0027]

[0028] Among them, δ λ,t Represents the disturbance of electricity price fluctuation, satisfying the uncertainty set Constraints;

[0029] The goal is to find a dispatching scheme that can maximize revenue in the worst case through a robust optimization method; the goal of the energy storage system is to maximize the profit of the energy storage equipment in the power market. The objective function formula is expressed as:

[0030]

[0031] in, represents the discharge power of the energy storage system at time t; represents the charging power of the energy storage system at time t; represents the market price taking into account the uncertainty of electricity price.

[0032] As a preferred solution of the new energy power storage coordinated dispatching method based on the spot market described in the present invention, the constraint condition includes a power balance constraint. At each time t, the power generation and load demand of the power system should be balanced. The formula is expressed as follows:

[0033]

[0034] Among them, P gen,t represents the power generated by the generator at time t, P load,t Represents the load demand at time t; Energy storage system capacity constraint: The storage energy of the energy storage system must be within its capacity limit, and the formula is expressed as:

[0035] E min ≤E t ≤E max

[0036] Among them, E t represents the energy state of the energy storage system at time t, E min and E max Respectively represent the minimum and maximum capacity of the energy storage system; the charging and discharging power constraint formula is expressed as:

[0037]

[0038] Among them, P charge,max and P discharge,max Respectively represent the maximum charging power and discharging power of the energy storage system;

[0039] By solving the robust optimization problem, the day-ahead scheduling plan is obtained to ensure that the system remains robust in the face of uncertainty.

[0040] Another object of the present invention is to provide a new energy power storage coordinated dispatching system based on the spot market, wherein:

[0041] Data collection and prediction module: acquire and predict new energy generation, electricity price and load data;

[0042] Optimization and scheduling decision module: Perform rolling optimization based on data to make charging and discharging decisions and scheduling plans;

[0043] Real-time monitoring and response module: real-time update of forecast and electricity price data, and real-time scheduling adjustment based on rolling optimization;

[0044] Feedback and evaluation module: Verify the effectiveness of scheduling decisions and optimize scheduling models through simulation and evaluation mechanisms.

[0045] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a new energy power storage coordinated scheduling method based on a spot market.

[0046] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a new energy power storage coordinated scheduling method based on a spot market.

[0047] Beneficial effects of the present invention: The method for coordinated dispatching of new energy power storage based on the spot market provided by the present invention, in the coordinated dispatching of energy storage systems with rolling optimization, the system needs to update the forecast information of new energy power generation and electricity prices in each time period, and optimize charging and discharging in combination with the status of the energy storage system. At the same time, the energy storage system can participate in frequency regulation services and respond quickly when frequency fluctuations occur in the power grid. This process maximizes the economic benefits of the energy storage system and improves the stability of the power grid through real-time scheduling and frequency regulation services. By combining electricity price arbitrage, frequency regulation services and rolling optimization, the energy storage system can effectively cope with uncertainty in a complex market environment and achieve the goal of maximizing benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 An overall flow chart of a new energy power storage coordinated dispatching method based on the spot market provided for the first embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] Example 1, reference Figure 1, which is an embodiment of the present invention, provides a new energy power storage coordinated scheduling method based on the spot market, comprising:

[0052] S1: Collect power system historical data and spot market forecast data.

[0053] Furthermore, the power system historical data and spot market forecast data include new energy power generation forecast data, power market data, energy storage system data, and load demand data.

[0054] Furthermore, electricity market data includes day-ahead market electricity price forecast data, real-time market electricity price data, and historical market price data.

[0055] Furthermore, the acquired historical data and forecast data are cleaned and processed, abnormal data are eliminated, and missing values ​​are filled; historical data are used to analyze the fluctuation range of sources of uncertainty.

[0056] Furthermore, the renewable energy generation predicted by new energy sources is intermittent and uncertain, so in scheduling, it is necessary to input the forecast data of new energy generation. These forecasts are usually supported by meteorological data of wind power and photovoltaic power generation, and ultra-short-term forecasting methods can be used to ensure the forecast accuracy. Wind power forecast: Input the predicted power generation P of the wind farm at each time t wind,t . Photovoltaic prediction: Input the predicted power generation of the photovoltaic power station P solar,t .

[0057] Furthermore, the data for new energy forecast can be expressed as:

[0058] P renewable,t =P wind,t +P solar,t

[0059] Furthermore, electricity price is an important factor affecting the dispatch of energy storage systems. Electricity price data usually comes from electricity trading prices in the day-ahead market and the real-time market. The input includes the following two types of electricity prices: day-ahead market electricity price: input day-ahead market electricity price forecast Real-time market electricity price: Input the real-time market electricity price forecast The energy storage system analyzes these electricity price data and decides to charge or discharge at different time points.

[0060] Furthermore, the total capacity of the energy storage system E max , represents the maximum electrical energy that the energy storage device can store.

[0061] Furthermore, the operating data of the energy storage system mainly includes the following parameters: Charging and discharging efficiency: the charging efficiency η of the energy storage system charge and discharge efficiency η discharge, usually less than 1, indicating energy loss. Charging and discharging power: the maximum charging power P of the energy storage system charge,max and the maximum discharge power P discharge,max In addition, the energy state E of the energy storage system needs to be considered t , and its update formula at different time steps is:

[0062]

[0063] Among them, Δt is the time step, which represents the duration of a scheduling cycle. The system also needs to input load forecast data P load,t , that is, the power demand forecast of the system at each time t. This helps the power system balance power generation and load demand.

[0064] It should be noted that the rolling optimization and scheduling in existing technologies are mostly based on relatively short-term electricity price forecasts and new energy generation forecasts. However, the actual fluctuations in the electricity market and power generation are often more drastic than predicted, resulting in insufficient response of the energy storage system in the face of emergencies and failure to fully utilize the flexibility of the energy storage system.

[0065] S2: Perform target modeling and input initial data.

[0066] Furthermore, target modeling includes defining optimization goals and defining optimization constraints based on the specific needs and physical constraints of the power system.

[0067] Furthermore, the optimization goals include maximizing revenue, minimizing costs, and system stability. After building the model, the forecast data of new energy power generation, electricity market data, energy storage system data, and load demand data are input. Among them, the forecast data uses an ultra-short-term forecasting method to ensure the forecast accuracy.

[0068] Going further, minimizing dispatch costs, if the goal is to minimize the overall operating cost of the power system by optimizing dispatch, then the objective function should be to minimize the total generation and storage.

[0069] Furthermore, in the electricity market, energy storage systems can maximize their revenue through "electricity price arbitrage". The goal is to make a profit by charging when electricity prices are low and discharging when electricity prices are high, participating in spot market price fluctuations. Objective function: maximize the total revenue of the energy storage system during the entire dispatch cycle:

[0070]

[0071] Among them, λ t Represents the electricity price at time t (can be the day-ahead market price or the real-time market price). Represents the discharge power of the energy storage system at time t. Represents the charging power of the energy storage system at time t. T represents the total duration of the scheduling cycle (e.g. 24 hours).

[0072] Furthermore, the cost objective function minimizes the total power generation and energy storage operating costs:

[0073]

[0074] Among them, C gen,t The power generation cost of the generator at time t (which can be calculated based on the fuel consumption rate and fuel price of the generator). storage,t The operating cost of the energy storage system at time t includes the loss cost of charging and discharging and the maintenance cost of the equipment.

[0075] It should be noted that in the coordinated dispatch of energy storage systems with rolling optimization, the system needs to update the forecast information of renewable energy generation and electricity prices in each time period, and optimize charging and discharging in combination with the status of the energy storage system. At the same time, the energy storage system can participate in frequency regulation services and respond quickly when the frequency of the power grid fluctuates. This process maximizes the economic benefits of the energy storage system and improves the stability of the power grid through real-time dispatch and frequency regulation services.

[0076] S3: Optimize the model and perform day-ahead scheduling and intraday scheduling.

[0077] Furthermore, day-ahead dispatching and intraday dispatching include: in the day-ahead dispatching stage, the system needs to formulate a power dispatching plan in advance; due to the uncertainty of fluctuations in new energy and electricity prices, day-ahead dispatching needs to take uncertainty factors into consideration and determine the optimal decision through robust optimization methods.

[0078] Furthermore, the uncertainty of renewable energy generation and electricity price is considered by using the robust optimization method by setting the uncertainty set; defining the uncertainty set All possible scenarios in the set need to satisfy robustness constraints and perform uncertainty modeling.

[0079] Furthermore, the intraday dispatch stage uses a rolling optimization method to continuously adjust the power dispatch plan based on more accurate short-term forecasts and real-time market prices to ensure the economy and stability of the system at every moment; the intraday dispatch stage is divided into multiple time periods, and each time period is re-dispatched and optimized based on the latest renewable energy forecast data and market electricity prices.

[0080] Furthermore, the uncertainty modeling includes the uncertainty of renewable energy generation. Represents the renewable energy output at time t, and the predicted output is P r,t , then its uncertainty is expressed as:

[0081]

[0082] Among them, δ r,t Represents the disturbance of renewable energy power generation fluctuations and satisfies the uncertainty set The constraints of electricity price uncertainty, is the electricity price at time t, and the predicted electricity price is λ t , then the uncertainty formula is expressed as:

[0083]

[0084] Among them, δ λ,t Represents the disturbance of electricity price fluctuation, satisfying the uncertainty set constraints.

[0085] Furthermore, the goal is to find a dispatching scheme that can still maximize revenue in the worst case through a robust optimization method; the goal of the energy storage system is to maximize the profit of the energy storage equipment in the power market, and the objective function formula is expressed as:

[0086]

[0087] in, represents the discharge power of the energy storage system at time t; represents the charging power of the energy storage system at time t; represents the market price taking into account the uncertainty of electricity price.

[0088] Furthermore, the constraints include power balance constraints. At each time t, the power generation and load demand of the power system must be balanced. The formula is expressed as:

[0089]

[0090] Among them, P gen,t represents the power generated by the generator at time t, P load,t Represents the load demand at time t; Energy storage system capacity constraint: The storage energy of the energy storage system must be within its capacity limit, and the formula is expressed as:

[0091] E min ≤E t ≤E max

[0092] Among them, E t represents the energy state of the energy storage system at time t, E min and E max Respectively represent the minimum and maximum capacity of the energy storage system; the charging and discharging power constraint formula is expressed as:

[0093]

[0094] Among them, P charge,max and P discharge,max They represent the maximum charging power and discharging power of the energy storage system respectively.

[0095] Furthermore, by solving the robust optimization problem, the day-ahead scheduling plan is obtained to ensure that the system remains robust in the face of uncertainty.

[0096] It should be noted that the system verifies the effectiveness of the energy storage system's charging and discharging strategy through simulation, especially its performance in dealing with fluctuations in renewable energy and changes in electricity prices. The simulation results can be used to adjust the electricity price threshold and other parameters to further optimize the benefits.

[0097] S4: Real-time monitoring and forecasting updates, and rolling optimization scheduling.

[0098] Furthermore, rolling optimization scheduling includes setting rolling optimization targets. The rolling optimization target of the energy storage system is to maximize the benefits obtained by the energy storage system through electricity price arbitrage and frequency regulation services in the next time period.

[0099] Furthermore, short-term forecast updates are carried out, and the forecast values ​​of new energy power generation and electricity prices are updated in real time in each time period, and real-time scheduling optimization is carried out: the rolling optimization method is used to adjust the system's charging and discharging strategy according to the latest data in each time period; the goal of rolling optimization is to maximize the system's real-time benefits under new forecast conditions through short-term adjustments; rolling optimization scheduling needs to meet the defined power balance, energy storage system capacity and charging and discharging power constraints.

[0100] Furthermore, the rolling optimization goal of the energy storage system is to maximize the benefits of the energy storage system through electricity price arbitrage and frequency regulation services in the next period of time. The objective function can be defined as:

[0101]

[0102] Among them, T r is the time window for rolling optimization (such as 1 hour). t It is the real-time electricity price. and are the charging and discharging power of the energy storage system at time t. Frequency regulation service income is the additional income obtained by the energy storage system by participating in grid frequency regulation.

[0103] In the new time period [t0+T r ,t0+2T r ] Before starting, use the updated new energy forecast, electricity price forecast and load forecast to re-solve the optimization problem and determine the objective function of the rolling optimization of the energy storage system's charging and discharging strategy in this time period as:

[0104]

[0105] Furthermore, the constraints of power balance, energy storage system status, and charge and discharge power also need to be met. If the actual operating data deviates from the previous forecast (such as load exceeding the forecast or insufficient new energy generation), the energy storage system can respond in real time by dynamically adjusting its charge and discharge strategy to cope with these deviations. Adjustment strategies may include: increasing discharge power to cope with increased load. Reducing charging power to retain more energy storage to cope with fluctuations in the next period.

[0106] Furthermore, if the load demand P load,t In reality, it is higher than the predicted value, and the energy storage system increases the discharge capacity To cope with the imbalance between supply and demand, the power generation of new energy will decrease. If the power generation of new energy renewable,t If actual power generation is lower than predicted, the energy storage system needs to reduce charging or start discharging immediately to compensate for the shortfall in power generation.

[0107] Furthermore, energy storage systems not only earn profits through electricity price arbitrage, but also earn additional profits by participating in the power grid's frequency regulation services (such as frequency regulation). In the power system, energy storage systems can quickly respond to changes in power demand and provide frequency regulation services to help balance the supply and demand of the power grid. Upward frequency regulation is the discharge of energy storage when the frequency rises. When the grid frequency drops, that is, when the power demand is higher than the supply, the energy storage system can quickly discharge and provide additional power to the grid to help restore balance. Downward frequency regulation is the charging of energy storage when the frequency drops. When the grid frequency rises and the supply is higher than the demand, the energy storage system can absorb excess power and charge to help reduce the excess power of the grid.

[0108] Furthermore, the energy storage system can obtain frequency regulation service income by participating in the frequency regulation market. This income is calculated based on the frequency regulation capacity provided by the energy storage system and is usually expressed in the form of electricity price as λ frequency Energy storage system charging and discharging optimization, the charging and discharging power decision in each time period can be expressed as:

[0109]

[0110] Among them, λ threshold It is a set electricity price threshold. When the electricity price exceeds this value, discharge is performed, and when it is lower than this value, charge is performed. and are the maximum discharge and charge powers of the energy storage system, respectively. 1{·} represents an indicator function, which takes the value 1 when the condition is true and 0 otherwise. Energy storage state constraint: the energy state of the energy storage system must be kept within a certain range to ensure that the system can respond to the energy storage state E when needed. t The update formula is:

[0111]

[0112] Among them, η charge and η discharge are the charging and discharging efficiencies respectively. Δt is the time step. The constraints on the energy storage state are:

[0113] E min ≤E t ≤E max

[0114] Among them, E min and E max The minimum and maximum energy capacities of the energy storage system are respectively. The dispatch plan is updated in a rolling manner. After each rolling optimization is completed, the energy storage system updates the dispatch plan based on the new electricity price forecast and load forecast. At the end of each rolling period, the system will re-forecast the next renewable energy generation and market electricity price, and adjust the charging and discharging strategy of the energy storage system.

[0115] It should be noted that the charging and discharging strategy of the energy storage system needs to be optimized in combination with the market electricity price, the state of the energy storage system and future forecasts. For electricity price arbitrage optimization, the goal of the energy storage system is to charge when the electricity price is low and discharge when the electricity price is high, so as to participate in market price arbitrage.

[0116] Example 2: The following is an embodiment of the present invention, which provides a new energy power storage coordinated scheduling method based on the spot market. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0117] This experiment collected historical load data of the power system in a certain region throughout 2023 and the corresponding spot market electricity price forecast data to simulate the coordinated dispatch of energy storage systems and renewable energy systems. The experiment is divided into four main steps, including data collection, target modeling, model optimization and dispatch, real-time monitoring and rolling optimization.

[0118] First, the experiment collected historical data including wind power and photovoltaic power generation as reference input for renewable energy. The data includes meteorological conditions such as wind speed and solar radiation intensity as well as actual power generation. At the same time, the spot market electricity price forecast data comes from the power trading platform of the intraday market and the real-time market, including the day-ahead electricity price and the real-time electricity price. These data will be used as the basic input for scheduling optimization, combined with the charging and discharging efficiency of the energy storage system, to build a complete scheduling decision model.

[0119] Next, through target modeling, the experiment defined the optimization goal of the energy storage system, which is the dual goal of maximizing economic benefits and power system stability. The experiment input the capacity of the energy storage system (100MWh), charging efficiency (90%), discharge efficiency (85%), as well as maximum charging power (20MW) and maximum discharge power (20MW). At the same time, load data is obtained from the system historical data, including the load demand curve throughout the day, wind power and photovoltaic power generation curves as inputs to the model.

[0120] At the beginning of the experiment, a preliminary optimization decision was made through the day-ahead scheduling model. In the day-ahead scheduling, combined with the forecast data of wind and photovoltaic power generation, the system will charge when the electricity price is low and discharge when the electricity price is high, so as to maximize the benefits. The intraday scheduling module of the experiment is based on rolling forecasts. By updating the renewable energy generation data and electricity price data in real time, the optimal scheduling plan of the energy storage system is recalculated every 15 minutes to ensure the economy and stability of each time period.

[0121] During the daily dispatch process, the real-time monitoring system adjusts the wind power and photovoltaic power generation forecast data according to the real-time changes in the weather, calculates the actual power generation deviation, and updates the charging and discharging strategy of the energy storage system. The core of rolling optimization dispatch is to ensure that the energy storage system can respond flexibly to the fluctuations of new energy and changes in electricity prices by continuously adjusting the dispatch decision. To verify the effect of this process, the experiment was run for 7 days, with 24-hour dispatch every day, and data updates and optimization adjustments every hour during the day.

[0122] In order to fully verify the effectiveness of the scheduling strategy, the experiment also designed a comparative experiment based on the traditional scheduling method, in which the energy storage system only charges and discharges according to the price information of the day-ahead market, without considering the real-time updated load demand and the volatility of renewable energy. By comparing the operating results of the new rolling optimization scheduling method with the traditional scheduling method, the advantages of the new method in terms of economic benefits and system stability can be verified. The obtained data are shown in Table 1.

[0123] Table 1 is a partial result table of control test.

[0124]

[0125] From the data in the above table, it can be seen that the rolling optimization scheduling method is significantly superior to the traditional scheduling method in many aspects. First, the charging and discharging power of the energy storage system is more flexible in the rolling optimization scheduling, with an average charging power of 19MW and an average discharging power of 12MW, while in the traditional scheduling, the charging and discharging power is relatively low, with an average charging power of 15MW and an average discharging power of 10MW. This shows that the rolling optimization scheduling can better utilize the capacity of the energy storage system by continuously updating the forecast data, thereby better responding to electricity price fluctuations and load demand.

[0126] Rolling optimization scheduling significantly reduces the amount of renewable energy curtailment. In the experiment, the amount of curtailment in the three-day period was 500MWh, 520MWh and 490MWh respectively under the traditional scheduling method, while the amount of curtailment in the rolling optimization scheduling method was 150MWh, 160MWh and 140MWh respectively. This shows that by updating the forecast data of renewable energy generation in real time, rolling optimization scheduling can better utilize wind and solar energy and reduce the waste of renewable energy.

[0127] In terms of economy, rolling optimization scheduling significantly reduces the operating cost of the system and improves the total revenue of the system. The total operating costs of the traditional scheduling method within 3 days were 2,200 yuan, 2,250 yuan and 2,300 yuan, respectively, while the operating costs of rolling optimization scheduling were 1,700 yuan, 1,750 yuan and 1,650 yuan, respectively, a reduction of about 25%. At the same time, rolling optimization scheduling also showed obvious advantages in revenue, with total revenues of 3,500 yuan, 3,600 yuan and 3,700 yuan within 3 days, respectively, an increase of 500 yuan, 500 yuan and 750 yuan compared with traditional scheduling. This shows that rolling optimization scheduling can more accurately control the charging and discharging timing of the energy storage system, ensure that the maximum revenue is obtained by discharging at peak electricity prices, and avoid losses caused by fluctuations in electricity prices. The load balance deviation of the system is also greatly reduced. The load balance deviation of the traditional scheduling method is 80MWh, 85MWh and 78MWh, while that of the rolling optimization scheduling is 30MWh, 35MWh and 28MWh respectively. This shows that the rolling optimization scheduling method can more accurately balance load and power generation through frequent scheduling updates, avoiding the risk of power system imbalance.

[0128] Example 3, reference Figure 1 , which is an embodiment of the present invention, provides a system for a new energy power storage coordinated dispatching method based on a spot market, including:

[0129] Data collection and prediction module: obtain and predict new energy generation, electricity price and load data.

[0130] Optimization and scheduling decision module: Perform rolling optimization based on data to make charging and discharging decisions and scheduling plans.

[0131] Real-time monitoring and response module: updates forecast and electricity price data in real time, and makes real-time scheduling adjustments based on rolling optimization.

[0132] Feedback and evaluation module: Verify the effectiveness of scheduling decisions and optimize scheduling models through simulation and evaluation mechanisms.

[0133] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0135] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0136] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A new energy power storage coordinated dispatching method based on the spot market, characterized in that: include: Collect power system historical data and spot market forecast data; Conduct target modeling and input initial data; Optimize the model to perform day-ahead and intraday scheduling; Real-time monitoring and forecasting updates, and rolling optimization scheduling.

2. The method for coordinated dispatching of new energy power storage based on the spot market according to claim 1, characterized in that: The power system historical data and spot market forecast data include new energy power generation forecast data, power market data, energy storage system data, and load demand data; Electricity market data includes day-ahead market electricity price forecast data, real-time market electricity price data, and historical market price data; Clean and process the acquired historical data and forecast data, remove abnormal data, and fill in missing values; use historical data to analyze the fluctuation range of sources of uncertainty.

3. The method for coordinated dispatching of new energy power storage based on the spot market according to claim 2, characterized in that: The target modeling includes defining optimization targets and defining optimization constraints according to specific requirements and physical constraints of the power system; The optimization goals include maximizing revenue, minimizing costs, and ensuring system stability. After the model is built, the forecast data of new energy power generation, electricity market data, energy storage system data, and load demand data are input. The forecast data uses an ultra-short-term forecasting method to ensure forecast accuracy.

4. The method for coordinated dispatching of new energy power storage based on the spot market according to claim 3, characterized in that: The day-ahead dispatch and intraday dispatch include that in the day-ahead dispatch stage, the system needs to formulate a power dispatch plan in advance; due to the uncertainty of the fluctuation of new energy and electricity prices, the day-ahead dispatch needs to consider the uncertainty factors and determine the optimal decision through the robust optimization method; Using robust optimization methods, the uncertainty of renewable energy generation and electricity prices is considered by setting uncertainty sets; defining uncertainty sets All possible scenarios in the set need to satisfy robustness constraints and perform uncertainty modeling; In the intraday dispatch stage, the power dispatch plan is continuously adjusted using a rolling optimization method based on more accurate short-term forecasts and real-time market prices to ensure the economy and stability of the system at every moment; The intraday scheduling stage is divided into multiple time periods. At the end of each time period, scheduling optimization is carried out again based on the latest renewable energy forecast data and market electricity prices.

5. The method for coordinated dispatching of new energy power storage based on the spot market according to claim 4, characterized in that: The rolling optimization scheduling includes setting a rolling optimization target, where the rolling optimization target of the energy storage system is to maximize the benefits obtained by the energy storage system through electricity price arbitrage and frequency regulation services in the next time period; Perform short-term forecast updates, update the forecast value of new energy power generation and electricity price in real time in each time period, optimize scheduling in real time, and use the rolling optimization method to adjust the system's charging and discharging strategy according to the latest data in each time period; maximize the system's real-time benefits under new forecast conditions through short-term adjustments; rolling optimization scheduling needs to meet the defined power balance, energy storage system capacity and charging and discharging power constraints.

6. The method for coordinated dispatching of new energy power storage based on the spot market according to claim 5, characterized in that: The uncertainty modeling includes the uncertainty of renewable energy generation, Represents the renewable energy output at time t, and the predicted output is P r,t , then its uncertainty is expressed as: Among them, δ r,t Represents the disturbance of renewable energy generation fluctuations and satisfies the uncertainty set The constraints of electricity price uncertainty, is the electricity price at time t, and the predicted electricity price is λ t , then the uncertainty formula is expressed as: Among them, δ λ,t Represents the disturbance of electricity price fluctuation, satisfying the uncertainty set Constraints; The goal is to find a dispatching scheme that can maximize revenue in the worst case through a robust optimization method; the goal of the energy storage system is to maximize the profit of the energy storage equipment in the electricity market. The objective function formula is expressed as: in, represents the discharge power of the energy storage system at time t; represents the charging power of the energy storage system at time t; represents the market price taking into account the uncertainty of electricity price.

7. The method for coordinated dispatching of new energy power storage based on the spot market according to claim 6, characterized in that: The constraints include power balance constraints. At each time t, the power generation and load demand of the power system must be balanced. The formula is: Among them, P gen,t represents the power generated by the generator at time t, P load,t represents the load demand at time t; the energy storage system capacity constraint, the storage energy of the energy storage system must be within its capacity limit, the formula is expressed as: AND min ≤E t ≤E max Among them, E t represents the energy state of the energy storage system at time t, E min and E max Respectively represent the minimum and maximum capacity of the energy storage system; the charging and discharging power constraint formula is expressed as: Among them, P charge,max and P discharge,max Respectively represent the maximum charging power and discharging power of the energy storage system; By solving the robust optimization problem, the day-ahead scheduling plan is obtained to ensure that the system remains robust in the face of uncertainty.

8. A system using the new energy power storage coordinated dispatching method based on the spot market as claimed in any one of claims 1 to 7, characterized in that: Data collection and prediction module: acquire and predict new energy generation, electricity price and load data; Optimization and scheduling decision module: Perform rolling optimization based on data to make charging and discharging decisions and scheduling plans; Real-time monitoring and response module: real-time update of forecast and electricity price data, and real-time scheduling adjustment based on rolling optimization; Feedback and evaluation module: Verify the effectiveness of scheduling decisions and optimize scheduling models through simulation and evaluation mechanisms.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the new energy power storage coordinated scheduling method based on the spot market described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the new energy power storage coordinated scheduling method based on the spot market described in any one of claims 1 to 7 are implemented.

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